Time Series Predictive Models for Opponent Behavior Modeling in Bilateral Negotiations

Time Series Predictive Models for Opponent Behavior Modeling in Bilateral Negotiations

Yesevi, Gevher (Özyeğin University)
Keskin, M.O. (TU Delft Interactive Intelligence; Özyeğin University)
Doğru, Anıl (Özyeğin University)
Aydoğan, Reyhan (TU Delft Interactive Intelligence; Özyeğin University)

Aydoğan, Reyhan (editor)
Criado, Natalia (editor)
Sanchez-Anguix, Victor (editor)
Lang, Jérôme (editor)
Serramia, Marc (editor)

2023

In agent-based negotiations, it is crucial to understand the opponent’s behavior and predict its bidding pattern to act strategically. Foreseeing the utility of the opponent’s coming offer provides valuable insight to the agent so that it can decide its next move wisely. Accordingly, this paper addresses predicting the opponent’s coming offers by employing two deep learning-based approaches: Long Short-Term Memory Networks and Transformers. The learning process has three different targets: estimating the agent’s utility of the opponent’s coming offer, estimating the agent’s utility of that without using opponent-related variables, and estimating the opponent’s utility of that by using opponent-related variables. This work reports the performances of these models that are evaluated in various negotiation scenarios. Our evaluation showed promising results regarding the prediction performance of the proposed methods.

Automated negotiation
Multi-agent systems
Time-series prediction
Utility prediction

http://resolver.tudelft.nl/uuid:cc42d036-dbe8-4ed5-b03a-1b8a619f47d0

https://doi.org/10.1007/978-3-031-21203-1_23

Springer

2023-07-01

9783031212024

PRIMA 2022: Principles and Practice of Multi-Agent Systems - 24th International Conference, Proceedings

24th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020, 2022-11-16 → 2022-11-18, Valencia , Spain

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 13753 LNAI

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 Gevher Yesevi, M.O. Keskin, Anıl Doğru, Reyhan Aydoğan